Chimeric antigen receptor (CAR)-T and NK cell immunotherapies have transformed cancer treatment, and recent studies suggest that the quality of the CAR-T/NK cell immunological synapse (IS) may serve as a functional biomarker for predicting therapeutic efficacy. Accurate detection and segmentation of CAR-T/NK IS structures using artificial neural networks (ANNs) can greatly increase the speed and reliability of IS quantification. However, a persistent challenge is the limited size of annotated microscopy datasets, which restricts the ability of ANNs to generalize. To address this challenge, we integrate two complementary data-augmentation frameworks. First, we employ Instance Aware Automatic Augmentation (IAAA), an automated, instance-preserving augmentation method that generates synthetic CAR-T/NK IS images and corresponding segmentation masks by applying optimized augmentation policies to original IS data. IAAA supports multiple imaging modalities (e.g., fluorescence and brightfield) and can be applied directly to CAR-T/NK IS images derived from patient samples. In parallel, we introduce a Semantic-Aware AI Augmentation (SAAA) pipeline that combines a diffusion-based mask generator with a Pix2Pix conditional image synthesizer. This second method enables the creation of diverse, anatomically realistic segmentation masks and produces high-fidelity CAR-T/NK IS images aligned with those masks, further expanding the training corpus beyond what IAAA alone can provide. Together, these augmentation strategies generate synthetic images whose visual and structural properties closely match real IS data, significantly improving CAR-T/NK IS detection and segmentation performance. By enhancing the robustness and accuracy of IS quantification, this work supports the development of more reliable imaging-based biomarkers for predicting patient response to CAR-T/NK immunotherapy.

翻译：嵌合抗原受体（CAR）-T和NK细胞免疫疗法已彻底改变了癌症治疗，近期研究表明CAR-T/NK细胞免疫突触（IS）的质量可作为预测治疗疗效的功能性生物标志物。利用人工神经网络（ANN）对CAR-T/NK免疫突触结构进行精确检测与分割，可显著提升IS定量分析的速度与可靠性。然而，当前持续存在的挑战在于标注显微图像数据集的规模有限，这制约了ANN的泛化能力。为应对这一挑战，我们整合了两种互补的数据增强框架。首先，我们采用实例感知自动增强（IAAA）方法——这是一种自动化且保持实例特征的增强技术，通过对原始IS数据应用优化后的增强策略，生成合成的CAR-T/NK免疫突触图像及对应分割掩码。IAAA支持多种成像模态（如荧光与明场成像），并可直接应用于源自患者样本的CAR-T/NK免疫突触图像。同时，我们提出了一种语义感知AI增强（SAAA）流程，该流程将基于扩散模型的掩码生成器与Pix2Pix条件图像合成器相结合。这第二种方法能够生成多样化、解剖结构真实的分割掩码，并产生与这些掩码对齐的高保真CAR-T/NK免疫突触图像，从而在IAAA单独生成的数据基础上进一步扩展训练语料库。综合来看，这些增强策略生成的合成图像在视觉与结构特性上与真实IS数据高度吻合，显著提升了CAR-T/NK免疫突触的检测与分割性能。通过增强IS定量分析的鲁棒性与准确性，本研究为开发更可靠的基于影像的生物标志物以预测患者对CAR-T/NK免疫疗法的反应提供了支持。